Genetic Adaptation and Yield Optimization in Soybean Cultivation
A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Plant Science".
Deadline for manuscript submissions: 25 December 2026 | Viewed by 258
Editor
Interests: plant breeding; genomic selection; genotype × environment interaction; enviromics; multi-environment trials (MET); climate adaptation in crops; soybean genomics; predictive modeling; artificial intelligence in agriculture; yield stability
Special Issue Information
Dear Colleagues,
This Special Issue, “Genetic Adaptation and Yield Optimization in Soybean Cultivation”, aims to provide a comprehensive and integrative perspective on the scientific and technological advances shaping modern soybean breeding under increasingly complex environmental conditions.
The primary focus of this Special Issue is the genetic basis of soybean adaptation and yield performance across diverse and dynamic agroecological environments. Emphasis is placed on understanding and modeling genotype × environment (G×E) interactions, enhancing yield stability, and improving prediction accuracy under climate variability.
We welcome original research and review articles addressing quantitative genetics, genomic selection, multi-environment trials (MET), enviromics, environmental characterization, crop modeling, high-throughput phenotyping, and artificial intelligence applications in breeding. Contributions exploring climate resilience, stress adaptation, mega-environment delineation, and target population of environments (TPE) refinement are particularly encouraged.
The purpose of this Special Issue is to consolidate interdisciplinary approaches that integrate genomic, phenotypic, and environmental data to optimize breeding decisions and accelerate genetic gain in soybean. By combining theoretical developments with applied case studies, this Special Issue seeks to advance predictive breeding frameworks suited to global production challenges.
While a substantial body of literature exists on soybean genomics, yield improvement, and G×E analysis, these domains are often treated separately. This Special Issue will supplement existing research by promoting integrative methodologies that unify genetics and structured environmental modeling within predictive frameworks. It will also provide updated perspectives on climate adaptation, environmental clustering, and machine learning applications, thereby extending classical breeding paradigms toward data-driven, environment-aware selection strategies.
We look forward to your valuable contributions.
Prof. Dr. Glauco Vieira Miranda
Guest Editor
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Keywords
- soybean breeding
- genetic adaptation
- genotype × environment interaction
- genomic prediction
- enviromics
- multi-environment trials
- climate resilience
- yield stability
- artificial intelligence in agriculture
- crop improvement
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